R Notebook
Libraries
PREP THE DATASET FOR ANALYSIS WVS 5 & 6 ####################
#read the data (Wave 5)
clean the data set
[1] "wave" "V1A" "V1B" "country_code" "V2A" "V3" "V4"
[8] "V4_CO" "V5" "V5_CO" "V6" "V6_CO" "V7" "V7_CO"
[15] "V8" "V8_CO" "V9" "V9_CO" "V10" "V11" "V12"
[22] "V13" "V14" "V15" "V16" "V17" "V18" "V19"
[29] "V20" "V21" "V22" "V23" "V24" "V25" "V26"
[36] "V27" "V28" "V29" "V30" "V31" "V32" "V33"
[43] "V34" "V35" "V36" "V37" "V38" "V39" "V40"
[50] "V41" "V42" "V43" "V43_01" "V43_02" "V43_03" "V43_04"
[57] "V43_05" "V43_06" "V43_07" "V43_08" "V43_09" "V43_10" "V43_11"
[64] "V43_12" "V43_13" "V43_14" "V43_15" "V43_16" "V43_17" "V43_18"
[71] "V43_19" "V43_20" "V43_21" "V43_22" "V43_23" "V43_24" "V43_25"
[78] "V43_26" "V43_27" "V43_28" "V43_29" "V43_30" "V44" "V45"
[85] "V46" "V47" "V48" "V49" "V50" "V51" "V52"
[92] "V53" "V54" "married" "children" "V57" "V58" "V59"
[99] "V60" "V61" "V62" "V63" "V64" "V65" "V66"
[106] "V67" "V68" "V69" "V69_HK" "V70" "V70_HK" "V71"
[113] "V72" "V73" "V73_HK" "V74" "V74_HK" "V75" "V76"
[120] "V77" "V78" "V79" "V80" "V81" "V82" "V83"
[127] "V84" "V85" "risktaking" "V87" "V88" "V89" "V90"
[134] "V91" "V92" "V93" "V94" "V95" "V96" "V97"
[141] "V98" "V99" "V100" "V101" "V102" "V103" "V104"
[148] "V105" "V106" "V107" "V108" "V109" "V110" "V111"
[155] "V112" "V113" "V114" "V115" "V116" "V117" "V118"
[162] "V119" "V120" "V121" "V122" "V123" "V124" "V125"
[169] "V126" "V127" "V128" "V129" "V130" "V130_CA_1" "V130_IQ_1"
[176] "V130_IQ_2" "V130_IQ_3" "V130_IQ_4" "V130_NZ_1" "V130_NZ_2" "V131" "V132"
[183] "V133" "V134" "V135" "V136" "V137" "V138" "V139"
[190] "V140" "V141" "V142" "V143" "V144" "V145" "V146_00"
[197] "V146_01" "V146_02" "V146_03" "V146_04" "V146_05" "V146_06" "V146_07"
[204] "V146_08" "V146_09" "V146_10" "V146_11" "V146_12" "V146_13" "V146_14"
[211] "V146_15" "V146_16" "V146_17" "V146_18" "V146_19" "V146_20" "V146_21"
[218] "V146_22" "V147" "V148" "V149" "V150" "V151" "V151_IQ_A"
[225] "V151_IQ_B" "V152" "V153" "V154" "V155" "V156" "V157"
[232] "V158" "V159" "V160" "V161" "V162" "V163" "V164"
[239] "V165" "V166" "V167" "V168" "V169" "V170" "V171"
[246] "V172" "V173" "V174" "V175" "V176" "V177" "V178"
[253] "V179" "V180" "V181" "V182" "V183" "V184" "V185"
[260] "V186" "V187" "V188" "V189" "V190" "V191" "V192"
[267] "V193" "V194" "V195" "V196" "V197" "V198" "V199"
[274] "V200" "V201" "V202" "V203" "V204" "V205" "V206"
[281] "V207" "V208" "V209" "V210" "V211" "V212" "V213A"
[288] "V213B" "V213C" "V213D" "V213E" "V213F" "V213G" "V213H"
[295] "V213K" "V213L" "V213M" "V213N" "V214" "V215" "V216"
[302] "V217" "V218" "V219" "V220" "V221" "V222" "V223"
[309] "V224" "V225" "V226" "V227" "V228" "V229" "V230"
[316] "V231" "V232" "V233" "V233A" "V234" "gender" "V236"
[323] "age" "education" "V238CS" "V239" "V240" "employed" "V242"
[330] "V242A_CO" "V243" "V244" "V245" "V246" "V247" "V248"
[337] "V249" "V250" "V251" "V252" "V252B" "V253" "V253CS"
[344] "V254" "V255" "V255CS" "V256" "V257" "V257B" "V257C"
[351] "V258" "V259" "V259A" "V260" "V261" "V262" "V263"
[358] "V264" "V265" "S024" "S025" "Y001" "Y002" "Y003"
[365] "SACSECVAL" "SECVALWGT" "RESEMAVAL" "WEIGHTB" "I_AUTHORITY" "I_NATIONALISM" "I_DEVOUT"
[372] "DEFIANCE" "WEIGHT1A" "I_RELIGIMP" "I_RELIGBEL" "I_RELIGPRAC" "DISBELIEF" "WEIGHT2A"
[379] "I_NORM1" "I_NORM2" "I_NORM3" "RELATIVISM" "WEIGHT3A" "I_TRUSTARMY" "I_TRUSTPOLICE"
[386] "I_TRUSTCOURTS" "SCEPTICISM" "WEIGHT4A" "I_INDEP" "I_IMAGIN" "I_NONOBED" "AUTONOMY"
[393] "WEIGHT1B" "I_WOMJOB" "I_WOMPOL" "I_WOMEDU" "EQUALITY" "WEIGHT2B" "I_HOMOLIB"
[400] "I_ABORTLIB" "I_DIVORLIB" "CHOICE" "WEIGHT3B" "I_VOICE1" "I_VOICE2" "I_VOI2_00"
[407] "VOICE" "WEIGHT4B" "S001" "S007" "S018" "S019" "S021"
[414] "COW" Read countrynames data from the CSV file (to decode the dataset 5)
Andorra Argentina Australia Brazil Bulgaria
1003 1002 1421 1500 1001
Burkina Faso Canada Chile China Colombia
1534 2164 1000 1991 3025
Cyprus (G) Egypt Ethiopia Finland France
1050 3051 1500 1014 1001
Georgia Germany Ghana Great Britain Guatemala
1500 2064 1534 1041 1000
Hong Kong Hungary India Indonesia Iran
1252 1007 2001 2015 2667
Iraq Italy Japan Jordan Malaysia
2701 1012 1096 1200 1201
Mali Mexico Moldova Morocco Netherlands
1534 1560 1046 1200 1050
New Zealand Norway Peru Poland Romania
954 1025 1500 1000 1776
Russia Rwanda Serbia and Montenegro Slovenia South Africa
2033 1507 1220 1037 2988
South Korea Spain Sweden Switzerland Taiwan
1200 1200 1003 1241 1227
Thailand Trinidad and Tobago Turkey Ukraine United States
1534 1002 1346 1000 1249
Uruguay Viet Nam Zambia
1000 1495 1500 #Read Dataset (Wave 6)
#rename variables
#decode daraset (Wave 6)
Algeria Argentina Armenia Australia Azerbaijan Belarus
1200 1030 1100 1477 1002 1535
Brazil Chile China Colombia Cyprus (G) Ecuador
1486 1000 2300 1512 1000 1202
Egypt Estonia Georgia Germany Ghana Haiti
1523 1533 1202 2046 1552 1996
Hong Kong India Iraq Japan Jordan Kazakhstan
1000 4078 1200 2443 1200 1500
Kuwait Kyrgyzstan Lebanon Libya Malaysia Mexico
1303 1500 1200 2131 1300 2000
Morocco Netherlands New Zealand Nigeria Pakistan Palestine
1200 1902 841 1759 1200 1000
Peru Philippines Poland Qatar Romania Russia
1210 1200 966 1060 1503 2500
Rwanda Singapore Slovenia South Africa South Korea Spain
1527 1972 1069 3531 1200 1189
Sweden Taiwan Thailand Trinidad and Tobago Tunisia Turkey
1206 1238 1200 999 1205 1605
Ukraine United States Uruguay Uzbekistan Yemen Zimbabwe
1500 2232 1000 1500 1000 1500 #combine the 2 dataset (Wave 6 + Wave 5)
#exclusion of participants and omission of missing data (na)
controll data
[1] 77
[1] 149626
[1] 15 99
1 2
71689 77937 create a categorical age variable (for example, to plot response frequencies by category)
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
56 377 530 3386 3309 3734 3373 3605 3766 3639 3858 3532 3584 3500 3252 3969 3059 3302 2959 2917 3585 3073 2883 3054 2737 3600
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66
2582 3114 2759 2597 3130 2570 2490 2453 2384 2990 2227 2407 2111 2140 2408 2069 2004 1909 1597 2202 1579 1764 1607 1416 1722 1352
67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
1155 1239 995 1380 907 1002 896 761 782 687 590 485 395 429 337 283 264 231 212 97 71 48 49 39 16 12
93 94 95 97 98 99
14 12 6 4 4 2
15-19 20-29 30-39 40-49 50-59 60-69 70-79 80+
7658 35843 31538 27679 21862 15031 7885 2130 create a categorical education variable (with fewer categories than the original)
incomplete or no primary education No Uni Uni
19354 70033 60239 Dichotomizing Variables: This helps estimating and interpreting the models later on…
PREP THE DATASET FOR ANALYSIS HARDSHIP ####################
read in file that contains hardship indicators manually collected from CIA factbook, WHO, and World Bank
[1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[22] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[43] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[64] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEControl the data
[1] 7.57837
[1] 24858.98
[1] 75.72305Plot histogram of all hardship indicators
log transform
changing variables into the same direction
Create the ‘hardship’ column in the ‘hardship’ data frame
Control the data
[1] -1.45915e-16
[1] -1.24345e-15
[1] -1.809769e-14Plot histogram of all hardship indicators after log transform
SUP MATERIALS:Correlation between hardship indicators
quartz_off_screen
2 Cronbach’s Alpha for Hardship Indicators
Number of categories should be increased in order to count frequencies.
Reliability analysis
Call: alpha(x = hardship_subset)
95% confidence boundaries
Reliability if an item is dropped:
Item statistics Merge the data matrix with the countryfacts dataset based on the country code
#Transformation of item risktaking
Risktaking as ordinal variable
Transform risk variable into T-score (mean = 50, sd = 10)
[1] 41.59569
[1] 3.198655
[1] NA
[1] 50Mixed-model WVS - Replication of Mata et al., 2016
intercept only model
age, sex
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(age) + factor(gender) + (1 + scale(age) + factor(gender) | country)
Data: WVS_data
Control: lmerControl(optimizer = "bobyqa")
REML criterion at convergence: 1089409
Scaled residuals:
Min 1Q Median 3Q Max
-2.4750 -0.7820 -0.0772 0.7523 3.2237
Random effects:
Groups Name Variance Std.Dev. Corr
country (Intercept) 6.5665 2.5625
scale(age) 0.7696 0.8773 0.31
factor(gender)1 0.8683 0.9318 0.12 0.25
Residual 84.6279 9.1993
Number of obs: 149626, groups: country, 77
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 51.3921 0.2946 76.2227 174.44 <2e-16 ***
scale(age) -2.0245 0.1040 74.0984 -19.46 <2e-16 ***
factor(gender)1 -2.3103 0.1183 73.3904 -19.52 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(g)
scale(age) 0.296
fctr(gndr)1 0.067 0.222 Summary model1
age, sex, and covariates (children, marital status, employement status, education)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(age) + factor(gender) + factor(children) +
factor(married) + factor(employed) + factor(education) +
(1 + scale(age) + factor(gender) + factor(children) + factor(married) +
factor(employed) + factor(education) | country)
Data: WVS_data
Control: lmerControl(optCtrl = list(maxfun = 30000), optimizer = "bobyqa")
REML criterion at convergence: 1087927
Scaled residuals:
Min 1Q Median 3Q Max
-2.56649 -0.78106 -0.08451 0.74253 3.15001
Random effects:
Groups Name Variance Std.Dev. Corr
country (Intercept) 5.4755 2.3400
scale(age) 0.5171 0.7191 0.23
factor(gender)1 0.9205 0.9594 0.00 0.25
factor(children)1 0.8306 0.9114 0.09 0.17 0.08
factor(married)1 0.4093 0.6398 0.18 0.45 0.54 0.25
factor(employed)1 0.2891 0.5377 0.01 0.06 0.03 -0.29 -0.23
factor(education)1 0.5935 0.7704 -0.17 0.07 0.10 -0.15 0.08 0.19
Residual 83.6510 9.1461
Number of obs: 149626, groups: country, 77
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 52.05267 0.28206 77.42485 184.547 < 2e-16 ***
scale(age) -1.47505 0.08894 72.28446 -16.584 < 2e-16 ***
factor(gender)1 -2.16455 0.12217 73.75436 -17.718 < 2e-16 ***
factor(children)1 -1.30522 0.12980 72.08925 -10.056 2.30e-15 ***
factor(married)1 -0.85358 0.09798 62.63868 -8.712 2.14e-12 ***
factor(employed)1 0.10892 0.08352 68.40295 1.304 0.197
factor(education)1 0.78343 0.11723 51.41904 6.683 1.67e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(g) fctr(g)1 fctr(c)1 fctr(mr)1 fctr(mp)1
scale(age) 0.202
fctr(gndr)1 -0.048 0.223
fctr(chld)1 0.001 0.029 0.015
fctr(mrrd)1 0.104 0.308 0.386 -0.049
fctr(mply)1 -0.055 0.083 0.088 -0.208 -0.154
fctr(dctn)1 -0.263 0.106 0.074 -0.081 0.036 0.065 Summary model2
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: T_score_risktaking ~ 1 + scale(age) * hardship + factor(gender) *
hardship + factor(children) + factor(married) + factor(employed) +
factor(education) + (1 + scale(age) + factor(gender) + factor(children) +
factor(married) + factor(employed) + factor(education) | country)
Data: WVS_data
Control: lmerControl(optCtrl = list(maxfun = 30000), optimizer = "bobyqa")
AIC BIC logLik deviance df.resid
1080162.5 1080548.9 -540042.2 1080084.5 148488
Scaled residuals:
Min 1Q Median 3Q Max
-2.55282 -0.78232 -0.08322 0.74348 3.14952
Random effects:
Groups Name Variance Std.Dev. Corr
country (Intercept) 5.3293 2.3085
scale(age) 0.3957 0.6290 0.17
factor(gender)1 0.7960 0.8922 -0.07 0.08
factor(children)1 0.7581 0.8707 0.05 0.05 0.06
factor(married)1 0.3949 0.6284 0.04 0.15 0.45 0.23
factor(employed)1 0.2850 0.5338 0.03 0.15 0.05 -0.26 -0.22
factor(education)1 0.5873 0.7663 -0.19 0.01 0.05 -0.13 0.08 0.19
Residual 83.7538 9.1517
Number of obs: 148527, groups: country, 76
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 52.09185 0.28118 76.31197 185.264 < 2e-16 ***
scale(age) -1.44448 0.08039 73.10104 -17.969 < 2e-16 ***
hardship 0.62678 0.37763 75.93540 1.660 0.1011
factor(gender)1 -2.15529 0.11633 67.47544 -18.527 < 2e-16 ***
factor(children)1 -1.27152 0.12673 73.26936 -10.034 2.12e-15 ***
factor(married)1 -0.84337 0.09781 62.48847 -8.623 3.11e-12 ***
factor(employed)1 0.09597 0.08372 68.53938 1.146 0.2557
factor(education)1 0.78009 0.11763 51.10237 6.632 2.06e-08 ***
scale(age):hardship 0.46635 0.11136 73.97759 4.188 7.69e-05 ***
hardship:factor(gender)1 0.29519 0.15287 69.94540 1.931 0.0575 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) scl(g) hrdshp fctr(g)1 fctr(c)1 fctr(mr)1 fctr(mp)1 fctr(d)1 scl():
scale(age) 0.154
hardship 0.066 0.033
fctr(gndr)1 -0.108 0.081 -0.005
fctr(chld)1 -0.032 -0.074 -0.003 -0.004
fctr(mrrd)1 0.008 0.094 -0.003 0.317 -0.068
fctr(mply)1 -0.039 0.140 0.005 0.104 -0.193 -0.146
fctr(dctn)1 -0.281 0.070 0.015 0.042 -0.068 0.036 0.062
scl(g):hrds 0.033 0.091 0.186 0.003 0.004 -0.005 -0.033 -0.004
hrdshp:f()1 -0.006 0.013 -0.106 0.071 -0.005 -0.011 0.022 0.001 0.037Summary model3
ANOVA
refitting model(s) with ML (instead of REML)Data: WVS_data
Models:
model0: T_score_risktaking ~ 1 + (1 | country)
model1: T_score_risktaking ~ 1 + scale(age) + factor(gender) + (1 + scale(age) + factor(gender) | country)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model0 3 1098757 1098787 -549375 1098751
model1 10 1089423 1089522 -544701 1089403 9348.1 7 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1refitting model(s) with ML (instead of REML)Data: WVS_data
Models:
model1: T_score_risktaking ~ 1 + scale(age) + factor(gender) + (1 + scale(age) + factor(gender) | country)
model2: T_score_risktaking ~ 1 + scale(age) + factor(gender) + factor(children) + factor(married) + factor(employed) + factor(education) + (1 + scale(age) + factor(gender) + factor(children) + factor(married) + factor(employed) + factor(education) | country)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
model1 10 1089423 1089522 -544701 1089403
model2 36 1087982 1088339 -543955 1087910 1492.9 26 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Error in anova.merMod(model2, model3) :
models were not all fitted to the same size of datasetDelete when submitting the code –> just for me to know where the document is
---
title: "R Notebook"
output: html_notebook
---

```{r}
rm(list = ls())
```


# Libraries
```{r}
library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
library(dplyr)
library(readxl)
library(ggrepel)
library(wordcloud)
library(lme4)
library(lmerTest)
library(reshape2)
library(patchwork)
library(psych)
```

####################
PREP THE DATASET FOR ANALYSIS WVS 5 & 6
####################

#read the data (Wave 5)
```{r}
# Data of Wave 5
WV5_data <- readRDS("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/F00007944-WV5_Data_R_v20180912.rds")

# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
WV5_data_df
```

# clean the data set
```{r}
#rename the variables
WV5_data <- WV5_data_df %>%
  rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
WV5_data

colnames(WV5_data)

#select only the variables of interest
WV5_data <- WV5_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, employed, education, married)
WV5_data
```
# Read countrynames data from the CSV file (to decode the dataset 5)
```{r}
countrynames <- read.csv("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/countrynames.txt", header = FALSE, as.is = TRUE)
colnames(countrynames) <- c("code", "name")

# Assuming WV5_data has a column named country_code
WV5_data$country <- countrynames$name[match(WV5_data$country_code, countrynames$code)]

# Check the frequency of each country in the new column
table(WV5_data$country)

# Display the updated WV5_data
print(WV5_data)
```

#Read Dataset (Wave 6)
```{r}
load("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 

print(WV6_data)
```
#rename variables
```{r}
WV6_data <- WV6_data %>%
  rename(wave = V1, gender = V240, age = V242,country_code = V2, risktaking = V76, children = V58, married = V57, employed = V229, education = V248)

#select only the variables of interest
WV6_data <- WV6_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, employed, education, married)
WV6_data
```
#decode daraset (Wave 6)
```{r}
countrynames = read.csv("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country = countrynames$name [match(WV6_data$country_code, countrynames$code)]
table(WV6_data$country)
WV6_data
```

#combine the 2 dataset (Wave 6 + Wave 5)
```{r}
WVS_data = rbind(WV5_data, WV6_data)
WVS_data
```

#exclusion of participants and omission of missing data (na)
```{r}
WVS_data = subset(WVS_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
### WVS_data <- na.omit(WVS_data) ### excluded because it is not in code from Mata et al., 2016

# Use the mutate function to change the country name
WVS_data <- WVS_data %>%
  mutate(country = ifelse(country == "Great Britain", "United Kingdom", country))
```

```{r}
head(WVS_data)
```

# controll data
```{r}
length(unique(WVS_data$country)) 
nrow(WVS_data) # number of individuals 
range(WVS_data$age, na.rm=TRUE) 
table(WVS_data$gender) # sex table(data$sex)/nrow(data) 
```

# create a categorical age variable (for example, to plot response frequencies by category)
```{r}
WVS_data$agecat[WVS_data$age<20]="15-19"
WVS_data$agecat[WVS_data$age>=20 & WVS_data$age <30] = "20-29"
WVS_data$agecat[WVS_data$age>=30 & WVS_data$age <40] = "30-39"
WVS_data$agecat[WVS_data$age>=40 & WVS_data$age <50] = "40-49"
WVS_data$agecat[WVS_data$age>=50 & WVS_data$age <60] = "50-59"
WVS_data$agecat[WVS_data$age>=60 & WVS_data$age <70] = "60-69"
WVS_data$agecat[WVS_data$age>=70 & WVS_data$age <80] = "70-79"
WVS_data$agecat[WVS_data$age>=80] = "80+"

table(WVS_data$age)
table(WVS_data$agecat)
```

# create a categorical education variable (with fewer categories than the original)
```{r}
# Neue Spalte 'education_cat' erstellen und initialisieren
WVS_data$education_cat <- NA

# Kategorien zuweisen basierend auf den Bildungsstufen
WVS_data$education_cat <- ifelse(WVS_data$education %in% c(1, 2), "incomplete or no primary education", 
                          ifelse(WVS_data$education %in% c(3, 4, 5, 6), "No Uni",
                          ifelse(WVS_data$education %in% c(7, 8, 9), "Uni", NA)))

# Tabelle der neuen Kategorien anzeigen
table(WVS_data$education_cat)
```

# Dichotomizing Variables: This helps estimating and interpreting the models later on...
```{r}
WVS_data$gender = ifelse(WVS_data$gender == 1, 0, 1) # sex: male vs. female
WVS_data$children = ifelse(WVS_data$children == 0, 0, 1) # children: no vs. yes
WVS_data$married = ifelse(WVS_data$married == 1, 1, 0) # married: yes vs. no
WVS_data$employed = ifelse(WVS_data$employed < 4, 1, 0) # employed: yes vs. no
WVS_data$education = ifelse(WVS_data$education < 4, 0, 1) # education: no primary vs. primary+ 
head(WVS_data)
```



####################
PREP THE DATASET FOR ANALYSIS HARDSHIP
####################

# read in file that contains hardship indicators manually collected from CIA factbook, WHO, and World Bank 
```{r}
library(readxl)

excel_path <- "/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/Hardship/Hardship_complete_2024.xlsx"
countryfacts <- read_excel(excel_path)

labels <- c("code", "country", "codeWVS", "Homicide", "GDP", "InfMort", "LifeExp", "GINI", "GenderPEdu")
names(countryfacts) <- labels

unique(WVS_data$country) %in% countryfacts$codeWVS # check that all countries in the subset of the WVS data are included in the countryfacts file

##### Haiti is not in the countryfacts list ##### 
countryfacts
WVS_data
```

# Control the data
```{r}
mean(countryfacts$Homicide, na.rm = TRUE)
mean(countryfacts$GDP, na.rm = TRUE)
mean(countryfacts$LifeExp, na.rm = TRUE)
```

# Plot histogram of all hardship indicators
```{r}
# Plot histogram of all hardship indicators
combined_plot <- NULL # Leeres Plot-Objekt erstellen

# Define the vector of labels for the items
items <- c("Homicide","GDP","InfMort","LifeExp","GINI","GenderPEdu")

# Loop durch jedes Item und füge das Histogramm zum kombinierten Plot hinzu
for (item in items) {
  # Erstelle ein Histogramm für das aktuelle Item
  plot <- ggplot(countryfacts, aes_string(x = item)) +
    geom_histogram(binwidth = 1, fill = "skyblue", color = "black") +
    labs(title = paste(item),
         x = item,
         y = "Frequency") +
    theme_minimal()
  
  # Füge das Histogramm zum kombinierten Plot hinzu
  if (is.null(combined_plot)) {
    combined_plot <- plot
  } else {
    combined_plot <- combined_plot + plot
  }
}

# Zeige den kombinierten Plot an
combined_plot
```


# log transform
```{r}
countryfacts$Homicide=log(countryfacts$Homicide)
countryfacts$GDP=log(countryfacts$GDP)
countryfacts$InfMort=log(countryfacts$InfMort)
countryfacts$LifeExp=log(countryfacts$LifeExp)
#countryfacts$GINI=log(countryfacts$GINI) # not transformed
countryfacts$GenderPEdu=log(countryfacts$GenderPEdu)

countryfacts
```

# changing variables into the same direction
```{r}
# Reverse Codierung
countryfacts$Homicide=scale(countryfacts$Homicide)
countryfacts$GDP=scale(-countryfacts$GDP)
countryfacts$InfMort=scale(countryfacts$InfMort)
countryfacts$LifeExp=scale(-countryfacts$LifeExp)
countryfacts$GINI=scale(countryfacts$GINI)
countryfacts$GenderPEdu=scale(-countryfacts$GenderPEdu)

countryfacts
```




# Create the 'hardship' column in the 'hardship' data frame
```{r}
countryfacts$hardship <- rowMeans(countryfacts[, c("Homicide", "GDP", "GINI", "LifeExp", "InfMort", "GenderPEdu")], na.rm = TRUE)

countryfacts
```


# Control the data
```{r}
mean(countryfacts$Homicide, na.rm = TRUE)
mean(countryfacts$GDP, na.rm = TRUE)
mean(countryfacts$LifeExp, na.rm = TRUE)
```

# Plot histogram of all hardship indicators after log transform
```{r}
# Plot histogram of all hardship indicators after log transform
# Leeres Plot-Objekt erstellen
combined_plot <- NULL

# Define the vector of labels for the items
items <- c("Homicide", "GDP", "GINI", "LifeExp", "InfMort", "GenderPEdu", "hardship")

# Loop durch jedes Item und füge das Histogramm zum kombinierten Plot hinzu
for (item in items) {
  # Erstelle ein Histogramm für das aktuelle Item
  plot <- ggplot(countryfacts, aes_string(x = item)) +
    geom_histogram(binwidth = 1, fill = "skyblue", color = "black") +
    labs(title = paste(item),
         x = item,
         y = "Frequency") +
    theme_minimal()
  
  # Füge das Histogramm zum kombinierten Plot hinzu
  if (is.null(combined_plot)) {
    combined_plot <- plot
  } else {
    combined_plot <- combined_plot + plot
  }
}

# Zeige den kombinierten Plot an
combined_plot
```


#################################################
# SUP MATERIALS:Correlation between hardship indicators 
```{r}
panel.cor = function(x, y, digits = 2, ...)
{
  usr = par("usr"); on.exit(par(usr))
  par(usr = c(0, 1, 0, 1))
  # correlation coefficient
  r = cor(x, y,use="complete.obs")
  txt = format(c(r, 0.123456789), digits = digits)[1]
  txt = paste("r= ", txt, sep = "")
  text(0.5, 0.6, txt)
  
  # p-value calculation
  p = cor.test(x, y,use="complete.obs")$p.value
  txt2 = format(c(p, 0.123456789), digits = digits)[1]
  txt2 = paste("p= ", txt2, sep = "")
  if(p<0.01) txt2 = paste("p ", "<0.01", sep = "")
  text(0.5, 0.4, txt2)
}
pairs(countryfacts[,4:10], upper.panel = panel.cor,las=1,cex.labels=.9) 
dev.print(postscript,"scatter_indicators.eps",width=8, height=8,horizontal=FALSE,onefile=FALSE)
```

# Cronbach's Alpha for Hardship Indicators
```{r}
library(psych)

# Subset der Hardship-Indikatoren aus dem countryfacts-Datensatz auswählen
hardship_subset <- countryfacts[, c("Homicide", "GDP", "InfMort", "LifeExp", "GINI", "GenderPEdu")]

# Cronbach's Alpha berechnen
alpha_result <- alpha(hardship_subset)
alpha_result
```

```{r}
# Ersetzen Sie "USA" durch "United States" im countryfacts-Datensatz
countryfacts$country[countryfacts$country == "USA"] <- "United States"
```

# Merge the data matrix with the countryfacts dataset based on the country code
```{r}
# Zusammenführen der 'hardship'-Variable von countryfacts mit WVS_data basierend auf dem Ländernamen
WVS_data <- merge(WVS_data, countryfacts[, c("country", "hardship")], by = "country", all.x = TRUE)

# Kontrolle des Zusammengeführten Datensatzes
head(WVS_data)
```

#Transformation of item risktaking
```{r}
WVS_data$risktaking = 6 - WVS_data$risktaking + 1
```

# Risktaking as ordinal variable
```{r}
# Define intervals for risktaking
interval <- cut(WVS_data$risktaking, breaks = c(-Inf, 1, 3, 5, Inf), labels = c("Very Low", "Low", "Medium", "High"), include.lowest = TRUE)

# Add the ordinal variable "Risktaking_ordinal" to the data frame
WVS_data$Risktaking_ordinal <- as.factor(interval)

# Display the updated data matrix
print(WVS_data)
```

# Transform risk variable into T-score (mean = 50, sd = 10)
```{r}
WVS_data$T_score_risktaking = 10*scale(WVS_data$risktaking, center=TRUE,scale=TRUE)+50

#Transform risk variable into Z score 
# Assuming T-scores have a mean of 50 and a standard deviation of 10
#WVS_data$Z_score_risktaking = (WVS_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
#print(WVS_data)

WVS_data <- WVS_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
WVS_data
```

```{r}
# Check for missing values
missing_values <- sum(!complete.cases(WVS_data))

# Remove missing values
data <- na.omit(data)
```

```{r}
mean(WVS_data$age)
mean(WVS_data$risktaking)
mean(WVS_data$hardship)
mean(WVS_data$T_score_risktaking)
```

# Mixed-model WVS - Replication of Mata et al., 2016
# intercept only model
```{r}
model0 = lmer(T_score_risktaking ~ 1 + (1|country),data = WVS_data)
summary_model0=summary(model0)
```

# age, sex 
```{r}
model1 <- lmer(T_score_risktaking ~ 1 + scale(age) + factor(gender) + (1 + scale(age) + factor(gender) | country), 
               data = WVS_data, 
                      control = lmerControl(optimizer = "bobyqa"))
summary_model1=summary(model1)
print(summary_model1) # Koeffizientenübersicht des Modells anzeigen
```

# Summary model1
```{r}
summary_model1 <- summary(model1)


# Gewünschte Werte extrahieren und formatieren
results_model1 <- data.frame(
  Predictor = c("Intercept", "Age", "Gender"),
  Estimate = c(summary_model1$coefficients["(Intercept)", "Estimate"],
               summary_model1$coefficients["scale(age)", "Estimate"],
               summary_model1$coefficients["factor(gender)1", "Estimate"]),
  SE = c(summary_model1$coefficients["(Intercept)", "Std. Error"],
          summary_model1$coefficients["scale(age)", "Std. Error"],
          summary_model1$coefficients["factor(gender)1", "Std. Error"]),
  T_score = c(summary_model1$coefficients["(Intercept)", "t value"],
              summary_model1$coefficients["scale(age)", "t value"],
              summary_model1$coefficients["factor(gender)1", "t value"]),
  p_value = c(summary_model1$coefficients["(Intercept)", "Pr(>|t|)"],
              summary_model1$coefficients["scale(age)", "Pr(>|t|)"],
              summary_model1$coefficients["factor(gender)1", "Pr(>|t|)"])
)

# p-Values
results_model1$p_value <- ifelse(results_model1$p_value < 0.001, "< .001", sprintf("%.3f", results_model1$p_value))

print(results_model1)
```

# age, sex, and covariates (children, marital status, employement status, education)
```{r}
model2 = lmer(T_score_risktaking ~ 1+scale(age)+factor(gender) + factor(children) + factor(married) + factor(employed) + factor(education)+ (1+scale(age)+factor(gender)+ factor(children) + factor(married) + factor(employed) + factor(education)|country),data = WVS_data,control=lmerControl(optCtrl=list(maxfun=30000),optimizer="bobyqa"))
summary_model2=summary(model2)

print(summary_model2) 
```

# Summary model2
```{r}
summary_model2 <- summary(model2)

# Gewünschte Werte extrahieren und formatieren
results_model2 <- data.frame(
  Predictor = c("Intercept", "Age", "Gender", "Parental status", "Marital status", "Occupational status", "Education"),
  Estimate = c(summary_model2$coefficients["(Intercept)", "Estimate"],
               summary_model2$coefficients["scale(age)", "Estimate"],
               summary_model2$coefficients["factor(gender)1", "Estimate"],
               summary_model2$coefficients["factor(children)1", "Estimate"],
               summary_model2$coefficients["factor(married)1", "Estimate"],
               summary_model2$coefficients["factor(employed)1", "Estimate"],
               summary_model2$coefficients["factor(education)1", "Estimate"]),
  SE = c(summary_model2$coefficients["(Intercept)", "Std. Error"],
          summary_model2$coefficients["scale(age)", "Std. Error"],
          summary_model2$coefficients["factor(gender)1", "Std. Error"],
          summary_model2$coefficients["factor(children)1", "Std. Error"],
          summary_model2$coefficients["factor(married)1", "Std. Error"],
          summary_model2$coefficients["factor(employed)1", "Std. Error"],
          summary_model2$coefficients["factor(education)1", "Std. Error"]),
  T_score = c(summary_model2$coefficients["(Intercept)", "t value"],
              summary_model2$coefficients["scale(age)", "t value"],
              summary_model2$coefficients["factor(gender)1", "t value"],
              summary_model2$coefficients["factor(children)1", "t value"],
              summary_model2$coefficients["factor(married)1", "t value"],
              summary_model2$coefficients["factor(employed)1", "t value"],
              summary_model2$coefficients["factor(education)1", "t value"]),
  p_value = c(summary_model2$coefficients["(Intercept)", "Pr(>|t|)"],
              summary_model2$coefficients["scale(age)", "Pr(>|t|)"],
              summary_model2$coefficients["factor(gender)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(children)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(married)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(employed)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(education)1", "Pr(>|t|)"])
)

# p-Values
results_model2$p_value <- ifelse(results_model2$p_value < 0.001, "< .001", sprintf("%.3f", results_model2$p_value))

print(results_model2)
```

```{r}
model3 <- lmer(T_score_risktaking ~ 1+scale(age)*hardship+factor(gender)*hardship + factor(children) + factor(married) + factor(employed) + factor(education)+ (1+scale(age)+factor(gender)+ factor(children) + factor(married) + factor(employed) + factor(education)|country),data = WVS_data,control=lmerControl(optCtrl=list(maxfun=30000),optimizer="bobyqa"),REML = FALSE)
summary_model3=summary(model3)

print(summary_model3)
```

# Summary model3
```{r}
# Zusammenfassung des Modells anzeigen
summary_model3 <- summary(model3)

# Gewünschte Werte extrahieren und formatieren
results_model3 <- data.frame(
  Predictor = c("Intercept", "Age", "Gender", "Parental status", "Marital status", "Occupational status", "Education", "Hardship", "Interaction: Gender * Hardship"),
  Estimate = c(summary_model3$coefficients["(Intercept)", "Estimate"],
               summary_model3$coefficients["scale(age)", "Estimate"],
               summary_model3$coefficients["factor(gender)1", "Estimate"],
               summary_model3$coefficients["factor(children)1", "Estimate"],
               summary_model3$coefficients["factor(married)1", "Estimate"],
               summary_model3$coefficients["factor(employed)1", "Estimate"],
               summary_model3$coefficients["factor(education)1", "Estimate"],
               summary_model3$coefficients["hardship", "Estimate"],
               summary_model3$coefficients["hardship:factor(gender)1", "Estimate"]),
  SE = c(summary_model3$coefficients["(Intercept)", "Std. Error"],
          summary_model3$coefficients["scale(age)", "Std. Error"],
          summary_model3$coefficients["factor(gender)1", "Std. Error"],
          summary_model3$coefficients["factor(children)1", "Std. Error"],
          summary_model3$coefficients["factor(married)1", "Std. Error"],
          summary_model3$coefficients["factor(employed)1", "Std. Error"],
          summary_model3$coefficients["factor(education)1", "Std. Error"],
          summary_model3$coefficients["hardship", "Std. Error"],
          summary_model3$coefficients["hardship:factor(gender)1", "Std. Error"]),
  T_score = c(summary_model3$coefficients["(Intercept)", "t value"],
              summary_model3$coefficients["scale(age)", "t value"],
              summary_model3$coefficients["factor(gender)1", "t value"],
              summary_model3$coefficients["factor(children)1", "t value"],
              summary_model3$coefficients["factor(married)1", "t value"],
              summary_model3$coefficients["factor(employed)1", "t value"],
              summary_model3$coefficients["factor(education)1", "t value"],
              summary_model3$coefficients["hardship", "t value"],
              summary_model3$coefficients["hardship:factor(gender)1", "t value"]),
  p_value = c(summary_model3$coefficients["(Intercept)", "Pr(>|t|)"],
              summary_model3$coefficients["scale(age)", "Pr(>|t|)"],
              summary_model3$coefficients["factor(gender)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(children)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(married)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(employed)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(education)1", "Pr(>|t|)"],
              summary_model3$coefficients["hardship", "Pr(>|t|)"],
              summary_model3$coefficients["hardship:factor(gender)1", "Pr(>|t|)"])
)

# Formatierung der p-Werte
results_model3$p_value <- ifelse(results_model3$p_value < 0.001, "< .001", sprintf("%.3f", results_model3$p_value))

# Ergebnisse anzeigen
print(results_model3)
```


# ANOVA
```{r}
anova(model0,model1)
anova(model1,model2)
anova(model2,model3) 
```


```{r}
WVS_data
```


```{r}
coefsallmodels=rbind(summary_model1$coefficients,
summary_model2$coefficients,
summary_model3$coefficients[c(1:2,4:8,3,9:10),])

write.csv(coefsallmodels,"coefsallmodels.csv")
```

# Delete when submitting the code --> just for me to know where the document is
```{r}
file_path <- file.path(getwd(), "coefsallmodels.csv")
file_path
```

```{r}
# Extrahieren der Koeffizienten-Tabelle für jedes Modell
coefficients_model0 <- summary(model0)$coefficients
coefficients_model1 <- summary(model1)$coefficients
coefficients_model2 <- summary(model2)$coefficients
coefficients_model3 <- summary(model3)$coefficients

# Filtern der erforderlichen Zeilen aus den Koeffizienten
coefficients_model0 <- coefficients_model0[rownames(coefficients_model0) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)"), ]
coefficients_model1 <- coefficients_model1[rownames(coefficients_model1) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)"), ]
coefficients_model2 <- coefficients_model2[rownames(coefficients_model2) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)", "factor(children)", "factor(married)", "factor(employed)", "factor(education)"), ]
coefficients_model3 <- coefficients_model3[rownames(coefficients_model3) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)", "factor(children)", "factor(married)", "factor(employed)", "factor(education)", "hardship", "scale(z_score_age):hardship", "factor(gender):hardship"), ]

# Zusammenführen der geschätzten Koeffizienten aus allen Modellen
coefs_all_models <- rbind(coefficients_model0, coefficients_model1, coefficients_model2, coefficients_model3)

# Erstellen einer Tabelle aus den Koeffizienten
results_table <- data.frame(
  Predictor = rownames(coefs_all_models),
  b = coefs_all_models[, "Estimate"],
  SE = coefs_all_models[, "Std. Error"],
  T_score = coefs_all_models[, "t value"],
  p_value = coefs_all_models[, "Pr(>|t|)"]
)

# Drucken der Ergebnistabelle
results_table
```



